Branch: refs/heads/master
Home: https://github.com/numpy/numpy
Commit: 127ae2f54d2c96fc7318fe12a3e2009e517828d1
https://github.com/numpy/numpy/commit/127ae2f54d2c96fc7318fe12a3e2009e517828d1
Author: Michael McNeil Forbes <michael.forbes+numpy@gmail.com>
Date: 2012-05-31 (Thu, 31 May 2012)
Changed paths:
M numpy/lib/function_base.py
M numpy/lib/tests/test_function_base.py
Log Message:
-----------
ENH: Add kwarg support for vectorize (tickets #2100, #1156, and #1487)
This is a substantial rewrite of vectorize to remove all introspection and
caching behaviour. This greatly simplifies the logic of the code, and allows
for much more generalized behaviour, simultaneously fixing tickets #1156,
#1487, and #2100. There will probably be a performance hit because caching is
no longer used (but should be able to be reinstated if needed).
As vectorize is a convenience function with poor performance in general,
perhaps this is okay. Rather than trying to inspect the function to determine
the number of arguments, defaults, and argument names, we just use the
arguments passed on the call to determine the behaviour on each call.
All tests pass and code is fully covered
Fixes:
Ticket #2100: kwarg support for vectorize
- API: Optional excluded argument to exclude some args from vectorization.
- Added documentation, examples, and coverage tests
- Added additional coverage test and base case for functions with no args
- Factored original behaviour into _vectorize_call
- Some minor documentation and error message corrections
Ticket #1156: Support vectorizing over instance methods
- No longer an issue since everything is determined by the call.
Ticket: #1487: result depends on execution order
- No longer caching, so the behaviour is as was expected.
ENH: Simple cache for vectorize
- Added simple cache to prevent vectorize from calling pyfunc twice on the first
argument when determining the output types and added regression test.
- Added documentation for excluded positional arguments.
- Documentation cleanups.
- Cleaned up variable names.
ENH: Performance improvements for backward compatibility of vectorize.
After some simple profiling, I found that the wrapping used to
support the caching of the previous commit wasted more time than
it saved, so I added a flag to allow the user to toggle. Moral:
caching makes sense only if the function is expensive and is off
by default.
I also compared performance with the original vectorize and opted
for keeping a cache of _ufunc if otypes is specified and there are
no kwargs/excluded vars. This case is easy to implement, and allows
users to reproduce (almost) the old performance characteristics if
needed. (The new version is about 5% slower in this case).
It would be much more complicated to add a similar cache in the case
where kwargs are used, and since a wrapper is used here, the
performance gain would be negligible (profiling showed that wrapping
was a more significant slowdown than the extra call to frompyfunc).
- API: Added cache kwarg which allows the user to toggle caching
of the first result.
- DOC: Added Notes section with a discussion of performance and a
warning that vectorize should not be used for performance.
- Added private _ufunc member to implement old-style of cache for
special case with no kwargs, excluded, and with otypes specified.
- Modified test case.
Partially address ticket #1982
- I tried to use hasattr(outputs, '__len__') rather than
isinstance(outputs, tuple) in order to allow for functions to return
lists. This, however, means that strings will get vectorized over
each character which breaks previous behaviour. Keeping old
behaviour for now.
Commit: c8beafda2251693396794a23601acf167a0e61d5
https://github.com/numpy/numpy/commit/c8beafda2251693396794a23601acf167a0e61d5
Author: Travis E. Oliphant <teoliphant@gmail.com>
Date: 2012-06-12 (Tue, 12 Jun 2012)
Changed paths:
M numpy/lib/function_base.py
M numpy/lib/tests/test_function_base.py
Log Message:
-----------
Merge pull request #290 from mforbes/new-vectorize-clean
ENH: Add kwarg support for vectorize (tickets #2100, #1156, and #1487) (clean)
Compare: https://github.com/numpy/numpy/compare/f2a7464e3b87...c8beafda2251